\name{RankingFoxDimmic}
\alias{RankingFoxDimmic}
\alias{RankingFoxDimmic-methods}
\alias{RankingFoxDimmic,matrix,numeric-method}
\alias{RankingFoxDimmic,matrix,factor-method}
\alias{RankingFoxDimmic,ExpressionSet,character-method}
\title{Ranking based on the t-statistic of Fox and Dimmic}
\description{
  Performs a two-sample Bayesian t test on a gene expression matrix using
the method of Fox and Dimmic (2006).}
\usage{
RankingFoxDimmic(x, y, type = "unpaired", m = 4, pvalues = TRUE, gene.names = NULL, ...)
}

\arguments{
  \item{x}{A \code{matrix} of gene expression values with rows
           corresponding to genes and columns corresponding to observations or alternatively an object of class \code{ExpressionSet}.}
  \item{y}{If \code{x} is a matrix, then \code{y} may be
           a \code{numeric} vector or a factor with at most two levels.\cr
           If \code{x} is an \code{ExpressionSet}, then \code{y}
           is a character specifying the phenotype variable in
           the output from \code{pData}.}
  \item{type}{\describe{
                \item{"unpaired":}{two-sample test, equal variances assumed.}
                }
                \code{"paired"} and \code{"unpaired"} are not possible
                for this kind of test.}
  \item{m}{The number of similarly expressed genes to use for calculating Bayesian variance
           and prior degrees of freedom. The default value suggested
           by Fox and Dimmic is currently 4, s. note.}
  \item{pvalues}{Should p-values be computed ? Default is \code{TRUE}.}
  \item{gene.names}{An optional vector of gene names.}
  \item{\dots}{Currently unused argument.}
}

\value{An object of class \link{GeneRanking}.}
\references{Fox, R.J., Dimmic, M.W. (2006). \cr
            A two sample Bayesian t-test for microarray data.
            \emph{BMC Bioinformatics, 7:126}}
\author{Martin Slawski  \cr
        Anne-Laure Boulesteix}
\note{Although the test of Fox and Dimmic is very similar to that
     of Baldi and Long; there are various slight differences,
     in particular with respect to the computation of the Bayesian variance.}
\seealso{
        \link{RepeatRanking},  \link{RankingTstat}, \link{RankingFC}, \link{RankingWelchT}, \link{RankingWilcoxon},
          \link{RankingBaldiLong},  \link{RankingLimma}, 
          \link{RankingEbam}, \link{RankingWilcEbam}, \link{RankingSam}, 
          \link{RankingShrinkageT}, \link{RankingSoftthresholdT}, 
          \link{RankingPermutation}}
\keyword{univar}         
\examples{
## Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### gene expression
xx <- toydata[-1,]
### run RankingFoxDimmic
FoxDimmic <- RankingFoxDimmic(xx, yy, type="unpaired")
}
